PACE eczema cohort comparisons

Summary of project + methods

This study is examining the association between DNA methylation and childhood eczema (up to ~10 years). Studies have data from different timepoints, so in the first instance we will classify individuals as ‘any eczema’ versus ‘no eczema’ according to all timepoints available up to age 10. Sub-analyses will be conducted that limits cases to those children who are diagnosed early in childhood (by ~2 years) or have persistent eczema (diagnosed by age 2 and have current eczema at age 8yrs).

The analyses will therefore use 3 binary definitions:

• Childhood AD - cases: AD by age 10, controls:no AD by age 10
• Early-onset AD - cases: AD by age 2, controls: no AD by age 2
• Persistent AD - cases: AD by age 2 and persisting to ~ 8-10yrs, controls: no AD by age 10

The association of each outcome with DNA methylation will be explored using logistic regression and 3 models with different covariates:

• model a: sex + batch (+ selection_factors) (+ ancestry)
• model b: sex + batch + maternal_age + maternal_smoking + maternal_SES + gestational_age (+selection_factors) (+ ancestry)
• model c: sex + batch + maternal_age + maternal_smoking + maternal_SES + gestational_age + cell_type (+ selection_factors) (+ ancestry)

In this report, the correlation between cohort effect estimates is examined. Further the relationship between effect estimate correlation and difference between prevalence estimates is assessed.

After the summary below and under each eczema type and model there are tables that show pairwise comparisons of each cohort. The correlation of effect estimates across the top 30 CpGs (the 30 CpGs with the lowest P values in the meta-analysis of EWAS) are shown (under the “effect_cor” column heading) alongside the difference in prevalence estimates between the cohorts (under the “prev_diff” column heading).

Using the top 30 CpG sites from the meta-analysis for each model, M-statistics were also calculated. These statistics give an indication of the study-wide heterogeneity across CpG sites, rather than typical heterogeneity statistics (such as I²) that assess heterogeneity across studies for single CpG sites.

Summary of models and association between effect estimate correlation and prevalence difference

Table 1: A summary of the association between effect size correlations and prevalence differences between cohorts

eczema-definition	model	n-tophits	maxp	top-hit-wilc-p	all-wilc-p
childhood	a	30	7.7e-05	1.8e-14	1.7e-14
childhood	b	30	5.7e-05	1.7e-14	1.7e-14
childhood	c	30	1.8e-04	1.9e-12	1.7e-12
early-onset	a	30	5.7e-05	1.4e-16	1.2e-16
early-onset	b	30	7.9e-05	1.4e-16	1.2e-16
early-onset	c	30	7.4e-05	1.4e-16	1.2e-16
persistent	a	30	7.9e-05	5.8e-11	2.9e-11
persistent	b	30	7.5e-05	5.8e-11	2.9e-11
persistent	c	30	7.9e-05	2.9e-11	2.9e-11

Correlations between effect sizes from different cohorts were calculated and then the association between these correlations and prevalence differences between the cohorts was estimated using a paired Wilcoxon signed rank test. This was analysis was performed using just the top 30 CpG sites from the meta-analysis (by P value) or using all CpG sites that overlapped between the individual cohort EWAS. n-tophits = number of CpGs selected from the meta-analysis, maxp = highest P value from the top CpGs, top-hit-wilc-p = P value from the Wilcoxon signed rank test when using the top CpGs only, all-wilc-p = P value from the Wilcoxon signed rank test when using all CpGs.

Childhood AD

Sample sizes + prevalence

cohort	N	N-cases	N-controls	prevalence
ALSPAC	673	343	330	51.0
CHS	197	50	147	25.4
EDEN	153	69	84	45.1
GENR	450	69	381	15.3
INMA	462	216	246	46.8
MoBa1	831	399	432	48.0
MoBa2	418	179	239	42.8
NEST_B	77	56	21	72.7
NEST_W	41	22	19	53.7
PREDO	792	54	738	6.8
VITO	78	23	55	29.5
GOYA	517	93	424	18.0
IOW	616	179	437	29.1
Total	5,305	1,752	3,553	33.0

Meta-regression results

Table 2: Assessing the impact of prevalence and samplesize on the influence a study has in the meta-analysis results

model	variable	beta	se	zval	pval	lower-ci	upper-ci
m1a	N	-0.00051	0.00024	-2.12	0.034	-0.00098	-3.9e-05
m1a	N_cases	-0.00031	0.00064	-0.49	0.628	-0.00157	9.5e-04
m1a	prevalence	0.00824	0.00352	2.34	0.019	0.00134	1.5e-02
m1a	definition	0.15479	0.15367	1.01	0.314	-0.14639	4.6e-01
m1a	definition_3grp	0.05426	0.15402	0.35	0.725	-0.24761	3.6e-01
m1b	N	-0.00043	0.00029	-1.49	0.136	-0.00099	1.3e-04
m1b	N_cases	-0.00032	0.00072	-0.44	0.656	-0.00173	1.1e-03
m1b	prevalence	0.00745	0.00415	1.79	0.073	-0.00069	1.6e-02
m1b	definition	0.14632	0.20392	0.72	0.473	-0.25335	5.5e-01
m1b	definition_3grp	0.03536	0.21558	0.16	0.870	-0.38717	4.6e-01
m1c	N	-0.00004	0.00020	-0.20	0.844	-0.00044	3.6e-04
m1c	N_cases	0.00031	0.00044	0.70	0.482	-0.00055	1.2e-03
m1c	prevalence	0.00223	0.00284	0.79	0.432	-0.00333	7.8e-03
m1c	definition	0.24785	0.12568	1.97	0.049	0.00153	4.9e-01
m1c	definition_3grp	0.16596	0.12615	1.32	0.188	-0.08129	4.1e-01

model a

Table 3: childhood AD, model a pairwise cohort comparsions

cohort1	cohort2	effect-cor	prev-diff
ALSPAC	CHS	0.724	25.59
ALSPAC	EDEN	0.518	5.87
CHS	EDEN	0.325	19.72
ALSPAC	GENR	0.700	35.63
CHS	GENR	0.506	10.05
EDEN	GENR	0.698	29.76
ALSPAC	GOYA	0.074	32.98
CHS	GOYA	-0.241	7.39
EDEN	GOYA	-0.147	27.11
GENR	GOYA	-0.157	2.66
ALSPAC	INMA	0.687	4.21
CHS	INMA	0.567	21.37
EDEN	INMA	0.597	1.66
GENR	INMA	0.779	31.42
GOYA	INMA	-0.078	28.76
ALSPAC	IOW	0.761	21.91
CHS	IOW	0.564	3.68
EDEN	IOW	0.575	16.04
GENR	IOW	0.753	13.73
GOYA	IOW	-0.153	11.07
INMA	IOW	0.855	17.69
ALSPAC	Meta	0.914	NA
CHS	Meta	0.711	NA
EDEN	Meta	0.634	NA
GENR	Meta	0.843	NA
GOYA	Meta	-0.013	NA
INMA	Meta	0.880	NA
IOW	Meta	0.908	NA
ALSPAC	MoBa1	0.708	2.95
CHS	MoBa1	0.509	22.63
EDEN	MoBa1	0.601	2.92
GENR	MoBa1	0.765	32.68
GOYA	MoBa1	-0.062	30.03
INMA	MoBa1	0.803	1.26
IOW	MoBa1	0.837	18.96
Meta	MoBa1	0.865	NA
ALSPAC	MoBa2	0.407	8.14
CHS	MoBa2	0.258	17.44
EDEN	MoBa2	0.482	2.28
GENR	MoBa2	0.490	27.49
GOYA	MoBa2	0.131	24.83
INMA	MoBa2	0.253	3.93
IOW	MoBa2	0.298	13.76
Meta	MoBa2	0.460	NA
MoBa1	MoBa2	0.284	5.19
ALSPAC	NEST_B	0.714	21.76
CHS	NEST_B	0.551	47.35
EDEN	NEST_B	0.677	27.63
GENR	NEST_B	0.713	57.39
GOYA	NEST_B	-0.121	54.74
INMA	NEST_B	0.831	25.97
IOW	NEST_B	0.833	43.67
Meta	NEST_B	0.845	NA
MoBa1	NEST_B	0.809	24.71
MoBa2	NEST_B	0.201	29.90
ALSPAC	NEST_W	0.041	2.69
CHS	NEST_W	-0.315	28.28
EDEN	NEST_W	0.421	8.56
GENR	NEST_W	0.396	38.33
GOYA	NEST_W	0.241	35.67
INMA	NEST_W	0.417	6.91
IOW	NEST_W	0.328	24.60
Meta	NEST_W	0.260	NA
MoBa1	NEST_W	0.373	5.64
MoBa2	NEST_W	0.204	10.84
NEST_B	NEST_W	0.336	19.07
ALSPAC	PREDO	0.793	44.15
CHS	PREDO	0.622	18.56
EDEN	PREDO	0.578	38.28
GENR	PREDO	0.786	8.52
GOYA	PREDO	0.083	11.17
INMA	PREDO	0.713	39.94
IOW	PREDO	0.729	22.24
Meta	PREDO	0.865	NA
MoBa1	PREDO	0.628	41.20
MoBa2	PREDO	0.562	36.00
NEST_B	PREDO	0.681	65.91
NEST_W	PREDO	0.248	46.84
ALSPAC	VITO	0.580	21.48
CHS	VITO	0.587	4.11
EDEN	VITO	0.121	15.61
GENR	VITO	0.505	14.15
GOYA	VITO	-0.075	11.50
INMA	VITO	0.549	17.27
IOW	VITO	0.500	0.43
Meta	VITO	0.607	NA
MoBa1	VITO	0.404	18.53
MoBa2	VITO	0.176	13.34
NEST_B	VITO	0.459	43.24
NEST_W	VITO	-0.104	24.17
PREDO	VITO	0.491	22.67

effect-cor = correlation between effect estimates of the top 30 CpGs, prev-diff = the estimated prevalence difference between cohorts. For more information see the Summary of project + methods section.

Figure 1: Correlation between the effect estimates of childhood eczema EWAS, model a, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.

Figure 2: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.

Figure 3: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.

model b

Table 4: childhood AD, model b pairwise cohort comparsions

cohort1	cohort2	effect-cor	prev-diff
ALSPAC	CHS	0.868	25.59
ALSPAC	EDEN	0.452	5.87
CHS	EDEN	0.256	19.72
ALSPAC	GENR	0.783	35.63
CHS	GENR	0.706	10.05
EDEN	GENR	0.559	29.76
ALSPAC	GOYA	0.504	32.98
CHS	GOYA	0.392	7.39
EDEN	GOYA	0.126	27.11
GENR	GOYA	0.243	2.66
ALSPAC	INMA	0.476	3.60
CHS	INMA	0.466	21.99
EDEN	INMA	0.412	2.27
GENR	INMA	0.371	32.04
GOYA	INMA	0.311	29.38
ALSPAC	IOW	0.659	21.71
CHS	IOW	0.626	3.88
EDEN	IOW	0.487	15.84
GENR	IOW	0.594	13.92
GOYA	IOW	0.205	11.27
INMA	IOW	0.601	18.11
ALSPAC	Meta	0.936	NA
CHS	Meta	0.862	NA
EDEN	Meta	0.564	NA
GENR	Meta	0.811	NA
GOYA	Meta	0.462	NA
INMA	Meta	0.675	NA
IOW	Meta	0.784	NA
ALSPAC	MoBa1	0.745	2.90
CHS	MoBa1	0.752	22.69
EDEN	MoBa1	0.394	2.97
GENR	MoBa1	0.619	32.73
GOYA	MoBa1	0.188	30.08
INMA	MoBa1	0.494	0.70
IOW	MoBa1	0.623	18.81
Meta	MoBa1	0.807	NA
ALSPAC	MoBa2	0.726	8.14
CHS	MoBa2	0.572	17.44
EDEN	MoBa2	0.727	2.28
GENR	MoBa2	0.757	27.49
GOYA	MoBa2	0.301	24.83
INMA	MoBa2	0.639	4.55
IOW	MoBa2	0.695	13.57
Meta	MoBa2	0.849	NA
MoBa1	MoBa2	0.653	5.24
ALSPAC	NEST_B	0.518	21.76
CHS	NEST_B	0.497	47.35
EDEN	NEST_B	0.482	27.63
GENR	NEST_B	0.442	57.39
GOYA	NEST_B	0.253	54.74
INMA	NEST_B	0.512	25.36
IOW	NEST_B	0.549	43.47
Meta	NEST_B	0.640	NA
MoBa1	NEST_B	0.500	24.66
MoBa2	NEST_B	0.494	29.90
ALSPAC	NEST_W	0.078	2.69
CHS	NEST_W	0.031	28.28
EDEN	NEST_W	0.508	8.56
GENR	NEST_W	0.134	38.33
GOYA	NEST_W	0.067	35.67
INMA	NEST_W	0.378	6.29
IOW	NEST_W	0.197	24.40
Meta	NEST_W	0.206	NA
MoBa1	NEST_W	0.024	5.59
MoBa2	NEST_W	0.396	10.84
NEST_B	NEST_W	0.237	19.07
ALSPAC	PREDO	0.833	44.15
CHS	PREDO	0.775	18.56
EDEN	PREDO	0.488	38.28
GENR	PREDO	0.759	8.52
GOYA	PREDO	0.383	11.17
INMA	PREDO	0.509	40.55
IOW	PREDO	0.673	22.44
Meta	PREDO	0.883	NA
MoBa1	PREDO	0.600	41.25
MoBa2	PREDO	0.726	36.00
NEST_B	PREDO	0.596	65.91
NEST_W	PREDO	0.215	46.84
ALSPAC	VITO	0.592	21.48
CHS	VITO	0.484	4.11
EDEN	VITO	0.117	15.61
GENR	VITO	0.598	14.15
GOYA	VITO	0.523	11.50
INMA	VITO	0.146	17.88
IOW	VITO	0.216	0.23
Meta	VITO	0.523	NA
MoBa1	VITO	0.291	18.58
MoBa2	VITO	0.433	13.34
NEST_B	VITO	0.173	43.24
NEST_W	VITO	-0.127	24.17
PREDO	VITO	0.452	22.67

effect-cor = correlation between effect estimates of the top 30 CpGs, prev-diff = the estimated prevalence difference between cohorts. For more information see the Summary of project + methods section.

Figure 4: Correlation between the effect estimates of childhood eczema EWAS, model b, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.

Figure 5: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.

Figure 6: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.

model c

Table 5: childhood AD, model c pairwise cohort comparsions

cohort1	cohort2	effect-cor	prev-diff
ALSPAC	CHS	0.62715	25.59
ALSPAC	EDEN	0.35254	5.87
CHS	EDEN	0.42439	19.72
ALSPAC	GENR	0.40856	35.63
CHS	GENR	0.51558	10.05
EDEN	GENR	0.55747	29.76
ALSPAC	GOYA	0.63175	32.98
CHS	GOYA	0.29616	7.39
EDEN	GOYA	0.18751	27.11
GENR	GOYA	0.21614	2.66
ALSPAC	INMA	0.57363	3.60
CHS	INMA	0.57320	21.99
EDEN	INMA	0.48464	2.27
GENR	INMA	0.40610	32.04
GOYA	INMA	0.37862	29.38
ALSPAC	IOW	0.71055	21.71
CHS	IOW	0.70628	3.88
EDEN	IOW	0.36014	15.84
GENR	IOW	0.33691	13.92
GOYA	IOW	0.41357	11.27
INMA	IOW	0.77705	18.11
ALSPAC	Meta	0.87786	NA
CHS	Meta	0.76469	NA
EDEN	Meta	0.52273	NA
GENR	Meta	0.61841	NA
GOYA	Meta	0.57536	NA
INMA	Meta	0.80060	NA
IOW	Meta	0.85849	NA
ALSPAC	MoBa1	0.66705	2.90
CHS	MoBa1	0.65597	22.69
EDEN	MoBa1	0.46307	2.97
GENR	MoBa1	0.63112	32.73
GOYA	MoBa1	0.42647	30.08
INMA	MoBa1	0.79494	0.70
IOW	MoBa1	0.70355	18.81
Meta	MoBa1	0.87096	NA
ALSPAC	MoBa2	0.67930	8.14
CHS	MoBa2	0.48053	17.44
EDEN	MoBa2	0.61650	2.28
GENR	MoBa2	0.72558	27.49
GOYA	MoBa2	0.52847	24.83
INMA	MoBa2	0.48847	4.55
IOW	MoBa2	0.55732	13.57
Meta	MoBa2	0.74569	NA
MoBa1	MoBa2	0.60287	5.24
ALSPAC	NEST_B	0.03950	21.76
CHS	NEST_B	-0.04220	47.35
EDEN	NEST_B	0.30469	27.63
GENR	NEST_B	0.09003	57.39
GOYA	NEST_B	-0.00019	54.74
INMA	NEST_B	-0.05804	25.36
IOW	NEST_B	-0.22943	43.47
Meta	NEST_B	0.01553	NA
MoBa1	NEST_B	-0.03350	24.66
MoBa2	NEST_B	-0.05051	29.90
ALSPAC	PREDO	0.78875	44.15
CHS	PREDO	0.73160	18.56
EDEN	PREDO	0.35564	38.28
GENR	PREDO	0.58113	8.52
GOYA	PREDO	0.46070	11.17
INMA	PREDO	0.61208	40.55
IOW	PREDO	0.69849	22.44
Meta	PREDO	0.86363	NA
MoBa1	PREDO	0.69942	41.25
MoBa2	PREDO	0.58478	36.00
NEST_B	PREDO	0.15223	65.91
ALSPAC	VITO	0.23321	21.48
CHS	VITO	0.44731	4.11
EDEN	VITO	0.29879	15.61
GENR	VITO	0.01956	14.15
GOYA	VITO	0.13552	11.50
INMA	VITO	0.30730	17.88
IOW	VITO	0.33803	0.23
Meta	VITO	0.34064	NA
MoBa1	VITO	0.24834	18.58
MoBa2	VITO	0.18651	13.34
NEST_B	VITO	0.05068	43.24
PREDO	VITO	0.21045	22.67

effect-cor = correlation between effect estimates of the top 30 CpGs, prev-diff = the estimated prevalence difference between cohorts. For more information see the Summary of project + methods section.

Figure 7: Correlation between the effect estimates of childhood eczema EWAS, model c, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.

Figure 8: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.

Figure 9: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.

Early-onset AD

Sample sizes + prevalence

cohort	N	N-cases	N-controls	prevalence
ALSPAC	667	169	498	25.3
CHS	197	41	156	20.8
DCHS	266	26	240	9.8
EDEN	142	48	94	33.8
GENR	742	171	571	23.0
INMA	348	83	265	23.9
IOW-F2	171	28	143	16.4
MoBa1	993	361	632	36.4
MoBa2	386	210	176	54.4
NEST_B	119	30	89	25.2
UKIDS	607	216	390	35.6
VITO	70	15	55	21.4
GOYA	517	39	478	7.5
IOW	667	99	569	14.8
Total	5,892	1,536	4,356	26.1

Meta-regression results

Table 6: Assessing the impact of prevalence and samplesize on the influence a study has in the meta-analysis results

model	variable	beta	se	zval	pval	lower-ci	upper-ci
m2a	N	-3.3e-04	0.00031	-1.06	0.287	-0.00094	2.8e-04
m2a	N_cases	-1.2e-03	0.00083	-1.41	0.159	-0.00279	4.6e-04
m2a	prevalence	-7.9e-03	0.00711	-1.12	0.264	-0.02188	6.0e-03
m2a	definition	5.3e-02	0.17708	0.30	0.767	-0.29456	4.0e-01
m2a	definition_3grp	9.5e-02	0.20515	0.46	0.644	-0.30739	5.0e-01
m2b	N	-2.2e-04	0.00034	-0.64	0.520	-0.00088	4.4e-04
m2b	N_cases	-4.4e-04	0.00093	-0.47	0.638	-0.00226	1.4e-03
m2b	prevalence	-1.4e-03	0.00784	-0.18	0.857	-0.01678	1.4e-02
m2b	definition	-7.9e-02	0.20903	-0.38	0.705	-0.48890	3.3e-01
m2b	definition_3grp	-8.1e-02	0.24473	-0.33	0.739	-0.56115	4.0e-01
m2c	N	-2.0e-04	0.00015	-1.35	0.176	-0.00050	9.1e-05
m2c	N_cases	-7.7e-05	0.00044	-0.18	0.861	-0.00094	7.9e-04
m2c	prevalence	5.9e-03	0.00328	1.79	0.074	-0.00056	1.2e-02
m2c	definition	-3.3e-02	0.09279	-0.35	0.723	-0.21473	1.5e-01
m2c	definition_3grp	-1.2e-01	0.09152	-1.26	0.208	-0.29451	6.4e-02

model a

Table 7: early-onset AD, model a pairwise cohort comparsions

cohort1	cohort2	effect-cor	prev-diff
ALSPAC	CHS	0.72	4.53
ALSPAC	DCHS	0.37	15.56
CHS	DCHS	0.31	11.04
ALSPAC	EDEN	0.74	8.47
CHS	EDEN	0.49	12.99
DCHS	EDEN	0.38	24.03
ALSPAC	GENR	0.56	2.29
CHS	GENR	0.38	2.23
DCHS	GENR	0.19	13.27
EDEN	GENR	0.48	10.76
ALSPAC	GOYA	0.56	17.79
CHS	GOYA	0.36	13.27
DCHS	GOYA	0.34	2.23
EDEN	GOYA	0.42	26.26
GENR	GOYA	0.62	15.50
ALSPAC	INMA	0.80	1.49
CHS	INMA	0.50	3.04
DCHS	INMA	0.49	14.08
EDEN	INMA	0.65	9.95
GENR	INMA	0.61	0.80
GOYA	INMA	0.76	16.31
ALSPAC	IOW	0.71	10.49
CHS	IOW	0.60	5.97
DCHS	IOW	0.58	5.07
EDEN	IOW	0.62	18.96
GENR	IOW	0.37	8.20
GOYA	IOW	0.23	7.30
INMA	IOW	0.62	9.01
ALSPAC	IOW-F2	0.64	8.96
CHS	IOW-F2	0.48	4.44
DCHS	IOW-F2	0.24	6.60
EDEN	IOW-F2	0.41	17.43
GENR	IOW-F2	0.33	6.67
GOYA	IOW-F2	0.36	8.83
INMA	IOW-F2	0.53	7.48
IOW	IOW-F2	0.57	1.53
ALSPAC	Meta	0.95	NA
CHS	Meta	0.71	NA
DCHS	Meta	0.49	NA
EDEN	Meta	0.77	NA
GENR	Meta	0.60	NA
GOYA	Meta	0.64	NA
INMA	Meta	0.91	NA
IOW	Meta	0.75	NA
IOW-F2	Meta	0.63	NA
ALSPAC	MoBa1	0.64	11.02
CHS	MoBa1	0.41	15.54
DCHS	MoBa1	0.45	26.58
EDEN	MoBa1	0.67	2.55
GENR	MoBa1	0.49	13.31
GOYA	MoBa1	0.63	28.81
INMA	MoBa1	0.86	12.50
IOW	MoBa1	0.55	21.51
IOW-F2	MoBa1	0.44	19.98
Meta	MoBa1	0.82	NA
ALSPAC	MoBa2	0.57	29.07
CHS	MoBa2	0.47	33.59
DCHS	MoBa2	0.71	44.63
EDEN	MoBa2	0.65	20.60
GENR	MoBa2	0.13	31.36
GOYA	MoBa2	0.31	46.86
INMA	MoBa2	0.49	30.55
IOW	MoBa2	0.65	39.56
IOW-F2	MoBa2	0.53	38.03
Meta	MoBa2	0.63	NA
MoBa1	MoBa2	0.54	18.05
ALSPAC	NEST_B	0.73	0.13
CHS	NEST_B	0.59	4.40
DCHS	NEST_B	0.42	15.44
EDEN	NEST_B	0.61	8.59
GENR	NEST_B	0.50	2.16
GOYA	NEST_B	0.70	17.67
INMA	NEST_B	0.83	1.36
IOW	NEST_B	0.52	10.37
IOW-F2	NEST_B	0.37	8.84
Meta	NEST_B	0.84	NA
MoBa1	NEST_B	0.82	11.14
MoBa2	NEST_B	0.43	29.19
ALSPAC	UKIDS	0.91	10.25
CHS	UKIDS	0.63	14.77
DCHS	UKIDS	0.21	25.81
EDEN	UKIDS	0.55	1.78
GENR	UKIDS	0.48	12.54
GOYA	UKIDS	0.59	28.04
INMA	UKIDS	0.79	11.73
IOW	UKIDS	0.53	20.74
IOW-F2	UKIDS	0.62	19.21
Meta	UKIDS	0.88	NA
MoBa1	UKIDS	0.65	0.77
MoBa2	UKIDS	0.37	18.82
NEST_B	UKIDS	0.72	10.37
ALSPAC	VITO	0.60	3.91
CHS	VITO	0.38	0.62
DCHS	VITO	0.34	11.65
EDEN	VITO	0.45	12.37
GENR	VITO	0.10	1.62
GOYA	VITO	0.43	13.89
INMA	VITO	0.60	2.42
IOW	VITO	0.49	6.59
IOW-F2	VITO	0.33	5.05
Meta	VITO	0.61	NA
MoBa1	VITO	0.55	14.93
MoBa2	VITO	0.53	32.98
NEST_B	VITO	0.54	3.78
UKIDS	VITO	0.51	14.16

effect-cor = correlation between effect estimates of the top 30 CpGs, prev-diff = the estimated prevalence difference between cohorts. For more information see the Summary of project + methods section.

Figure 10: Correlation between the effect estimates of early-onset eczema EWAS, model a, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.

Figure 11: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.

Figure 12: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.

model b

Table 8: early-onset AD, model b pairwise cohort comparsions

cohort1	cohort2	effect-cor	prev-diff
ALSPAC	CHS	0.464	4.53
ALSPAC	DCHS	-0.011	15.90
CHS	DCHS	0.369	11.38
ALSPAC	EDEN	0.514	8.47
CHS	EDEN	0.581	12.99
DCHS	EDEN	0.323	24.37
ALSPAC	GENR	0.673	2.29
CHS	GENR	0.359	2.23
DCHS	GENR	-0.035	13.61
EDEN	GENR	0.563	10.76
ALSPAC	GOYA	0.063	17.79
CHS	GOYA	0.344	13.27
DCHS	GOYA	0.524	1.89
EDEN	GOYA	0.039	26.26
GENR	GOYA	0.024	15.50
ALSPAC	INMA	0.775	1.79
CHS	INMA	0.528	2.73
DCHS	INMA	0.384	14.11
EDEN	INMA	0.631	10.26
GENR	INMA	0.658	0.50
GOYA	INMA	0.211	16.00
ALSPAC	IOW	0.406	10.28
CHS	IOW	0.234	5.75
DCHS	IOW	-0.034	5.63
EDEN	IOW	0.169	18.74
GENR	IOW	0.189	7.98
GOYA	IOW	-0.099	7.52
INMA	IOW	0.489	8.48
ALSPAC	IOW-F2	0.571	8.88
CHS	IOW-F2	0.057	4.36
DCHS	IOW-F2	-0.041	7.02
EDEN	IOW-F2	0.102	17.35
GENR	IOW-F2	0.355	6.59
GOYA	IOW-F2	-0.037	8.91
INMA	IOW-F2	0.493	7.09
IOW	IOW-F2	0.438	1.39
ALSPAC	Meta	0.955	NA
CHS	Meta	0.552	NA
DCHS	Meta	0.176	NA
EDEN	Meta	0.566	NA
GENR	Meta	0.665	NA
GOYA	Meta	0.139	NA
INMA	Meta	0.882	NA
IOW	Meta	0.536	NA
IOW-F2	Meta	0.580	NA
ALSPAC	MoBa1	0.791	11.10
CHS	MoBa1	0.423	15.63
DCHS	MoBa1	0.011	27.00
EDEN	MoBa1	0.273	2.63
GENR	MoBa1	0.529	13.39
GOYA	MoBa1	-0.008	28.89
INMA	MoBa1	0.579	12.89
IOW	MoBa1	0.415	21.38
IOW-F2	MoBa1	0.424	19.98
Meta	MoBa1	0.797	NA
ALSPAC	MoBa2	0.684	29.07
CHS	MoBa2	0.327	33.59
DCHS	MoBa2	0.082	44.97
EDEN	MoBa2	0.298	20.60
GENR	MoBa2	0.319	31.36
GOYA	MoBa2	-0.200	46.86
INMA	MoBa2	0.503	30.86
IOW	MoBa2	0.523	39.34
IOW-F2	MoBa2	0.439	37.95
Meta	MoBa2	0.702	NA
MoBa1	MoBa2	0.708	17.97
ALSPAC	NEST_B	0.547	0.13
CHS	NEST_B	0.353	4.40
DCHS	NEST_B	0.281	15.78
EDEN	NEST_B	0.208	8.59
GENR	NEST_B	0.173	2.16
GOYA	NEST_B	0.289	17.67
INMA	NEST_B	0.649	1.66
IOW	NEST_B	0.527	10.15
IOW-F2	NEST_B	0.443	8.75
Meta	NEST_B	0.642	NA
MoBa1	NEST_B	0.448	11.23
MoBa2	NEST_B	0.350	29.19
ALSPAC	UKIDS	0.871	10.25
CHS	UKIDS	0.540	14.77
DCHS	UKIDS	0.077	26.15
EDEN	UKIDS	0.447	1.78
GENR	UKIDS	0.523	12.54
GOYA	UKIDS	0.202	28.04
INMA	UKIDS	0.809	12.04
IOW	UKIDS	0.471	20.52
IOW-F2	UKIDS	0.508	19.13
Meta	UKIDS	0.899	NA
MoBa1	UKIDS	0.637	0.85
MoBa2	UKIDS	0.539	18.82
NEST_B	UKIDS	0.601	10.37
ALSPAC	VITO	0.479	3.91
CHS	VITO	0.038	0.62
DCHS	VITO	-0.470	11.99
EDEN	VITO	0.197	12.37
GENR	VITO	0.252	1.62
GOYA	VITO	-0.291	13.89
INMA	VITO	0.048	2.12
IOW	VITO	-0.093	6.37
IOW-F2	VITO	0.115	4.97
Meta	VITO	0.299	NA
MoBa1	VITO	0.478	15.01
MoBa2	VITO	0.283	32.98
NEST_B	VITO	-0.053	3.78
UKIDS	VITO	0.208	14.16

effect-cor = correlation between effect estimates of the top 30 CpGs, prev-diff = the estimated prevalence difference between cohorts. For more information see the Summary of project + methods section.

Figure 13: Correlation between the effect estimates of early-onset eczema EWAS, model b, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.

Figure 14: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.

Figure 15: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.

model c

Table 9: early-onset AD, model c pairwise cohort comparsions

cohort1	cohort2	effect-cor	prev-diff
ALSPAC	CHS	0.5173	4.53
ALSPAC	DCHS	0.0607	15.90
CHS	DCHS	0.4605	11.38
ALSPAC	EDEN	0.5178	8.47
CHS	EDEN	0.5985	12.99
DCHS	EDEN	0.2443	24.37
ALSPAC	GENR	0.5021	2.29
CHS	GENR	0.6697	2.23
DCHS	GENR	0.1961	13.61
EDEN	GENR	0.6859	10.76
ALSPAC	GOYA	0.3591	17.79
CHS	GOYA	0.2074	13.27
DCHS	GOYA	0.0931	1.89
EDEN	GOYA	-0.0086	26.26
GENR	GOYA	0.1570	15.50
ALSPAC	INMA	0.5337	1.79
CHS	INMA	0.7530	2.73
DCHS	INMA	0.3702	14.11
EDEN	INMA	0.6319	10.26
GENR	INMA	0.8378	0.50
GOYA	INMA	0.0266	16.00
ALSPAC	IOW	0.6093	10.28
CHS	IOW	0.5160	5.75
DCHS	IOW	0.0437	5.63
EDEN	IOW	0.5477	18.74
GENR	IOW	0.5974	7.98
GOYA	IOW	0.1333	7.52
INMA	IOW	0.7487	8.48
ALSPAC	IOW-F2	0.2976	8.88
CHS	IOW-F2	0.4564	4.36
DCHS	IOW-F2	0.1521	7.02
EDEN	IOW-F2	0.5387	17.35
GENR	IOW-F2	0.5272	6.59
GOYA	IOW-F2	-0.2518	8.91
INMA	IOW-F2	0.5156	7.09
IOW	IOW-F2	0.3806	1.39
ALSPAC	Meta	0.8016	NA
CHS	Meta	0.7673	NA
DCHS	Meta	0.2351	NA
EDEN	Meta	0.7356	NA
GENR	Meta	0.8446	NA
GOYA	Meta	0.2361	NA
INMA	Meta	0.8722	NA
IOW	Meta	0.7661	NA
IOW-F2	Meta	0.5236	NA
ALSPAC	MoBa1	0.5626	11.10
CHS	MoBa1	0.6755	15.63
DCHS	MoBa1	0.3529	27.00
EDEN	MoBa1	0.4900	2.63
GENR	MoBa1	0.6470	13.39
GOYA	MoBa1	0.3150	28.89
INMA	MoBa1	0.7054	12.89
IOW	MoBa1	0.6422	21.38
IOW-F2	MoBa1	0.2879	19.98
Meta	MoBa1	0.8110	NA
ALSPAC	MoBa2	0.5196	29.07
CHS	MoBa2	0.2449	33.59
DCHS	MoBa2	0.0737	44.97
EDEN	MoBa2	0.3981	20.60
GENR	MoBa2	0.2523	31.36
GOYA	MoBa2	0.2560	46.86
INMA	MoBa2	0.2476	30.86
IOW	MoBa2	0.5140	39.34
IOW-F2	MoBa2	0.2832	37.95
Meta	MoBa2	0.4610	NA
MoBa1	MoBa2	0.2624	17.97
ALSPAC	NEST_B	0.5412	0.13
CHS	NEST_B	0.5905	4.40
DCHS	NEST_B	0.2607	15.78
EDEN	NEST_B	0.4961	8.59
GENR	NEST_B	0.5697	2.16
GOYA	NEST_B	0.1642	17.67
INMA	NEST_B	0.6139	1.66
IOW	NEST_B	0.3710	10.15
IOW-F2	NEST_B	0.1080	8.75
Meta	NEST_B	0.6942	NA
MoBa1	NEST_B	0.5343	11.23
MoBa2	NEST_B	0.1217	29.19
ALSPAC	UKIDS	0.7279	10.25
CHS	UKIDS	0.7402	14.77
DCHS	UKIDS	0.1058	26.15
EDEN	UKIDS	0.5831	1.78
GENR	UKIDS	0.7375	12.54
GOYA	UKIDS	0.3411	28.04
INMA	UKIDS	0.7111	12.04
IOW	UKIDS	0.5899	20.52
IOW-F2	UKIDS	0.4299	19.13
Meta	UKIDS	0.8861	NA
MoBa1	UKIDS	0.6560	0.85
MoBa2	UKIDS	0.2739	18.82
NEST_B	UKIDS	0.7121	10.37
ALSPAC	VITO	0.3307	3.91
CHS	VITO	-0.2391	0.62
DCHS	VITO	-0.4712	11.99
EDEN	VITO	0.1425	12.37
GENR	VITO	0.0423	1.62
GOYA	VITO	0.1611	13.89
INMA	VITO	-0.0088	2.12
IOW	VITO	0.3068	6.37
IOW-F2	VITO	0.0417	4.97
Meta	VITO	0.1880	NA
MoBa1	VITO	0.1394	15.01
MoBa2	VITO	0.2746	32.98
NEST_B	VITO	-0.0166	3.78
UKIDS	VITO	0.1430	14.16

effect-cor = correlation between effect estimates of the top 30 CpGs, prev-diff = the estimated prevalence difference between cohorts. For more information see the Summary of project + methods section.

Figure 16: Correlation between the effect estimates of early-onset eczema EWAS, model c, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.

Figure 17: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.

Figure 18: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.

Persistent AD

Sample sizes + prevalence

cohort	N	N-cases	N-controls	prevalence
ALSPAC	662	125	537	18.9
CHS	197	35	162	17.8
EDEN	126	18	108	14.3
GENR	415	39	376	9.4
MoBa1	519	87	432	16.8
MoBa2	237	61	176	25.7
VITO	66	11	55	16.7
GOYA	517	33	484	6.4
IOW	462	25	437	5.4
Total	3,201	434	2,767	13.6

Meta-regression results

Table 10: Assessing the impact of prevalence and samplesize on the influence a study has in the meta-analysis results

model	variable	beta	se	zval	pval	lower-ci	upper-ci
m3a	N	-0.00024	0.00027	-0.89	0.37	-0.00077	0.00029
m3a	N_cases	-0.00027	0.00158	-0.17	0.86	-0.00336	0.00282
m3a	prevalence	0.01235	0.00762	1.62	0.11	-0.00258	0.02728
m3a	definition	-0.05261	0.17375	-0.30	0.76	-0.39316	0.28794
m3a	definition_3grp	-0.08742	0.19265	-0.45	0.65	-0.46501	0.29017
m3b	N	-0.00042	0.00024	-1.75	0.08	-0.00089	0.00005
m3b	N_cases	-0.00140	0.00150	-0.93	0.35	-0.00434	0.00154
m3b	prevalence	0.00991	0.00817	1.21	0.22	-0.00609	0.02592
m3b	definition	-0.15004	0.16706	-0.90	0.37	-0.47747	0.17740
m3b	definition_3grp	-0.22939	0.16026	-1.43	0.15	-0.54350	0.08471
m3c	N	-0.00021	0.00033	-0.64	0.52	-0.00086	0.00044
m3c	N_cases	-0.00133	0.00166	-0.80	0.42	-0.00458	0.00192
m3c	prevalence	-0.00349	0.00863	-0.40	0.69	-0.02040	0.01342
m3c	definition	-0.08385	0.20665	-0.41	0.68	-0.48887	0.32118
m3c	definition_3grp	-0.16820	0.20856	-0.81	0.42	-0.57697	0.24057

model a

Table 11: persistent AD, model a pairwise cohort comparsions

cohort1	cohort2	effect-cor	prev-diff
ALSPAC	CHS	0.589	1.116
ALSPAC	EDEN	0.267	4.596
CHS	EDEN	-0.104	3.481
ALSPAC	GENR	0.788	9.485
CHS	GENR	0.415	8.369
EDEN	GENR	0.115	4.888
ALSPAC	GOYA	0.566	12.499
CHS	GOYA	0.538	11.384
EDEN	GOYA	0.151	7.903
GENR	GOYA	0.457	3.015
ALSPAC	IOW	0.536	13.471
CHS	IOW	0.276	12.355
EDEN	IOW	0.048	8.874
GENR	IOW	0.533	3.986
GOYA	IOW	0.190	0.972
ALSPAC	Meta	0.932	NA
CHS	Meta	0.643	NA
EDEN	Meta	0.157	NA
GENR	Meta	0.869	NA
GOYA	Meta	0.605	NA
IOW	Meta	0.642	NA
ALSPAC	MoBa1	0.586	2.119
CHS	MoBa1	0.561	1.003
EDEN	MoBa1	-0.166	2.477
GENR	MoBa1	0.607	7.365
GOYA	MoBa1	0.241	10.380
IOW	MoBa1	0.532	11.352
Meta	MoBa1	0.763	NA
ALSPAC	MoBa2	0.528	6.856
CHS	MoBa2	0.410	7.972
EDEN	MoBa2	-0.040	11.453
GENR	MoBa2	0.541	16.341
GOYA	MoBa2	0.610	19.355
IOW	MoBa2	0.367	20.327
Meta	MoBa2	0.658	NA
MoBa1	MoBa2	0.427	8.975
ALSPAC	VITO	0.396	2.216
CHS	VITO	0.170	1.100
EDEN	VITO	0.157	2.381
GENR	VITO	0.415	7.269
GOYA	VITO	0.176	10.284
IOW	VITO	0.208	11.255
Meta	VITO	0.467	NA
MoBa1	VITO	0.214	0.096
MoBa2	VITO	0.577	9.072

effect-cor = correlation between effect estimates of the top 30 CpGs, prev-diff = the estimated prevalence difference between cohorts. For more information see the Summary of project + methods section.

Figure 19: Correlation between the effect estimates of persistent eczema EWAS, model a, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.

Figure 20: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.

Figure 21: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.

model b

Table 12: persistent AD, model b pairwise cohort comparsions

cohort1	cohort2	effect-cor	prev-diff
ALSPAC	CHS	0.5602	1.12
ALSPAC	EDEN	0.3047	4.60
CHS	EDEN	-0.0056	3.48
ALSPAC	GENR	0.7850	9.48
CHS	GENR	0.4789	8.37
EDEN	GENR	0.2270	4.89
ALSPAC	GOYA	0.6058	12.50
CHS	GOYA	0.4684	11.38
EDEN	GOYA	0.3189	7.90
GENR	GOYA	0.4308	3.01
ALSPAC	IOW	0.4910	13.57
CHS	IOW	0.3674	12.46
EDEN	IOW	0.3370	8.98
GENR	IOW	0.5328	4.09
GOYA	IOW	0.2302	1.07
ALSPAC	Meta	0.9274	NA
CHS	Meta	0.6078	NA
EDEN	Meta	0.2931	NA
GENR	Meta	0.8478	NA
GOYA	Meta	0.6121	NA
IOW	Meta	0.6360	NA
ALSPAC	MoBa1	0.5652	2.05
CHS	MoBa1	0.5013	0.94
EDEN	MoBa1	-0.0313	2.54
GENR	MoBa1	0.5309	7.43
GOYA	MoBa1	0.2461	10.44
IOW	MoBa1	0.4589	11.52
Meta	MoBa1	0.7507	NA
ALSPAC	MoBa2	0.4231	6.86
CHS	MoBa2	0.0625	7.97
EDEN	MoBa2	0.0586	11.45
GENR	MoBa2	0.3304	16.34
GOYA	MoBa2	0.4391	19.36
IOW	MoBa2	0.3887	20.43
Meta	MoBa2	0.5317	NA
MoBa1	MoBa2	0.3945	8.91
ALSPAC	VITO	0.3447	2.22
CHS	VITO	-0.0691	1.10
EDEN	VITO	-0.0578	2.38
GENR	VITO	0.2397	7.27
GOYA	VITO	0.0946	10.28
IOW	VITO	0.0071	11.36
Meta	VITO	0.3710	NA
MoBa1	VITO	0.2426	0.16
MoBa2	VITO	0.4350	9.07

effect-cor = correlation between effect estimates of the top 30 CpGs, prev-diff = the estimated prevalence difference between cohorts. For more information see the Summary of project + methods section.

Figure 22: Correlation between the effect estimates of persistent eczema EWAS, model b, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.

Figure 23: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.

Figure 24: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.

model c

Table 13: persistent AD, model c pairwise cohort comparsions

cohort1	cohort2	effect-cor	prev-diff
ALSPAC	CHS	0.789	18.88
ALSPAC	EDEN	0.543	4.60
CHS	EDEN	0.325	14.29
ALSPAC	GENR	0.801	9.48
CHS	GENR	0.590	9.40
EDEN	GENR	0.479	4.89
ALSPAC	GOYA	0.767	12.50
CHS	GOYA	0.611	6.38
EDEN	GOYA	0.316	7.90
GENR	GOYA	0.552	3.01
ALSPAC	IOW	0.583	13.57
CHS	IOW	0.509	5.31
EDEN	IOW	0.757	8.98
GENR	IOW	0.509	4.09
GOYA	IOW	0.239	1.07
ALSPAC	Meta	0.975	NA
CHS	Meta	0.809	NA
EDEN	Meta	0.617	NA
GENR	Meta	0.844	NA
GOYA	Meta	0.729	NA
IOW	Meta	0.703	NA
ALSPAC	MoBa1	0.861	2.05
CHS	MoBa1	0.796	16.83
EDEN	MoBa1	0.548	2.54
GENR	MoBa1	0.732	7.43
GOYA	MoBa1	0.538	10.44
IOW	MoBa1	0.671	11.52
Meta	MoBa1	0.910	NA
ALSPAC	MoBa2	0.724	6.86
CHS	MoBa2	0.747	25.74
EDEN	MoBa2	0.355	11.45
GENR	MoBa2	0.528	16.34
GOYA	MoBa2	0.669	19.36
IOW	MoBa2	0.434	20.43
Meta	MoBa2	0.758	NA
MoBa1	MoBa2	0.722	8.91
ALSPAC	VITO	0.274	2.22
CHS	VITO	0.137	16.67
EDEN	VITO	0.050	2.38
GENR	VITO	0.343	7.27
GOYA	VITO	0.033	10.28
IOW	VITO	0.184	11.36
Meta	VITO	0.263	NA
MoBa1	VITO	0.080	0.16
MoBa2	VITO	0.174	9.07

effect-cor = correlation between effect estimates of the top 30 CpGs, prev-diff = the estimated prevalence difference between cohorts. For more information see the Summary of project + methods section.

Figure 25: Correlation between the effect estimates of persistent eczema EWAS, model c, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.

Figure 26: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.

Figure 27: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.